{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ADKY4re5Kx-5"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "S2AOrHzjK0_L"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "56dF5DnkKx0a"
      },
      "source": [
        "# Credit Card Interest\n",
        "\n",
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Credit_Card_Interest.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Credit_Card_Interest.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rTFa-AE9KxFC"
      },
      "source": [
        "Let’s imagine that you would like to estimate the interest rate on your credit card one year from now. Suppose the current prime rate is 2% and your credit card company charges you 10% plus prime. Given the strength of the current economy, you believe that the Federal Reserve is more likely to raise interest rates than not. The Fed will meet eight times in the next twelve months and will either raise the federal funds rate by 0.25% or leave it at the previous level. \n",
        "\n",
        "We use the binomial distribution to model your credit card’s interest rate at the end of the twelve-month period. Specifically, we’ll use the TensorFlow Probability Binomial distribution class  with the following parameters: total_count = 8 (number of trials or meetings), probs = {.6, .7, .8, .9}, for our range of estimates about the probability of the Fed raising the federal funds rate by 0.25% at each meeting."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6C5X22NiK9r2"
      },
      "source": [
        "### Dependencies \u0026 Prerequisites"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 33
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 13522,
          "status": "ok",
          "timestamp": 1537573807035,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "sZzHCBk6Kti8",
        "outputId": "95c441a0-ab7e-416b-feec-884f6ab133c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Up-to-date, stable  TFP version installed\n"
          ]
        }
      ],
      "source": [
        "#@title TensorFlow Probability Installation settings { display-mode: \"form\" }\n",
        "TFP_Installation = \"Stable TFP\" #@param [\"Most Recent TFP\", \"Stable TFP\", \"Stable TFP-GPU\", \"Most Recent TFP-GPU\", \"TFP Already Installed\"]\n",
        "\n",
        "if TFP_Installation == \"Most Recent TFP\":\n",
        "    !pip install -q tfp-nightly\n",
        "    print(\"Most recent TFP version installed\")\n",
        "elif TFP_Installation == \"Stable TFP\":\n",
        "    !pip install -q --upgrade tensorflow-probability\n",
        "    print(\"Up-to-date, stable  TFP version installed\")\n",
        "elif TFP_Installation == \"Stable TFP-GPU\":\n",
        "    !pip install -q --upgrade tensorflow-probability-gpu\n",
        "    print(\"Up-to-date, stable TFP-GPU version installed\")\n",
        "    print(\"(make sure GPU is properly configured)\")\n",
        "elif TFP_Installation == \"Most Recent TFP-GPU\":\n",
        "    !pip install -q tfp-nightly-gpu\n",
        "    print(\"Most recent TFP-GPU version installed\")\n",
        "    print(\"(make sure GPU is properly configured)\")\n",
        "elif TFP_Installation == \"TFP Already Installed\":\n",
        "    print(\"TFP already installed in this environment\")\n",
        "    pass\n",
        "else:\n",
        "    print(\"Installation Error: Please select a viable TFP installation option.\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "MFezX4J_LAJe"
      },
      "outputs": [],
      "source": [
        "#@title Imports and Global Variables (make sure to run this cell)  { display-mode: \"form\" }\n",
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n",
        "import warnings\n",
        "warnings.filterwarnings(warning_status)\n",
        "with warnings.catch_warnings():\n",
        "    warnings.filterwarnings(warning_status, category=DeprecationWarning)\n",
        "    warnings.filterwarnings(warning_status, category=UserWarning)\n",
        "\n",
        "import numpy as np\n",
        "import os\n",
        "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n",
        "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n",
        "import matplotlib.axes as axes;\n",
        "from matplotlib.patches import Ellipse\n",
        "%matplotlib inline\n",
        "import seaborn as sns; sns.set_context('notebook')\n",
        "notebook_screen_res = 'png' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n",
        "%config InlineBackend.figure_format = notebook_screen_res\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "# Eager Execution\n",
        "use_tf_eager = True #@param {type:\"boolean\"}\n",
        "\n",
        "\n",
        "# Use try/except so we can easily re-execute the whole notebook.\n",
        "if use_tf_eager:\n",
        "  try:\n",
        "    tf.compat.v1.enable_eager_execution()\n",
        "  except:\n",
        "    reset_session()\n",
        "\n",
        "\n",
        "import tensorflow_probability as tfp\n",
        "tfd = tfp.distributions\n",
        "tfb = tfp.bijectors\n",
        " \n",
        "\n",
        "def default_session_options(enable_gpu_ram_resizing=True,\n",
        "                            enable_xla=False):\n",
        "  \"\"\"Creates default options for Graph-mode session.\"\"\"\n",
        "  config = tf.ConfigProto()\n",
        "  config.log_device_placement = True\n",
        "  if enable_gpu_ram_resizing:\n",
        "    # `allow_growth=True` makes it possible to connect multiple\n",
        "    # colabs to your GPU. Otherwise the colab malloc's all GPU ram.\n",
        "    config.gpu_options.allow_growth = True\n",
        "  if enable_xla:\n",
        "    # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n",
        "    config.graph_options.optimizer_options.global_jit_level = (\n",
        "        tf.OptimizerOptions.ON_1)\n",
        "  return config\n",
        "\n",
        "\n",
        "def reset_session(options=None):\n",
        "  \"\"\"Creates a new global, interactive session in Graph-mode.\"\"\"\n",
        "  if tf.executing_eagerly():\n",
        "    return\n",
        "  global sess\n",
        "  try:\n",
        "    tf.reset_default_graph()\n",
        "    sess.close()\n",
        "  except:\n",
        "    pass\n",
        "  if options is None:\n",
        "    options = default_session_options()\n",
        "  sess = tf.InteractiveSession(config=options)\n",
        " \n",
        "\n",
        "def evaluate(tensors):\n",
        "  \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n",
        "  Args:\n",
        "  tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n",
        "    `namedtuple` or combinations thereof.\n",
        " \n",
        "  Returns:\n",
        "    ndarrays: Object with same structure as `tensors` except with `Tensor` or\n",
        "      `EagerTensor`s replaced by Numpy `ndarray`s.\n",
        "  \"\"\"\n",
        "  if tf.executing_eagerly():\n",
        "    return tf.contrib.framework.nest.pack_sequence_as(\n",
        "        tensors,\n",
        "        [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n",
        "         for t in tf.contrib.framework.nest.flatten(tensors)])\n",
        "  return sess.run(tensors)\n",
        "\n",
        "\n",
        "class _TFColor(object):\n",
        "  \"\"\"Enum of colors used in TF docs.\"\"\"\n",
        "  red = '#F15854'\n",
        "  blue = '#5DA5DA'\n",
        "  orange = '#FAA43A'\n",
        "  green = '#60BD68'\n",
        "  pink = '#F17CB0'\n",
        "  brown = '#B2912F'\n",
        "  purple = '#B276B2'\n",
        "  yellow = '#DECF3F'\n",
        "  gray = '#4D4D4D'\n",
        "  def __getitem__(self, i):\n",
        "    return [\n",
        "        self.red,\n",
        "        self.orange,\n",
        "        self.green,\n",
        "        self.blue,\n",
        "        self.pink,\n",
        "        self.brown,\n",
        "        self.purple,\n",
        "        self.yellow,\n",
        "        self.gray,\n",
        "    ][i % 9]\n",
        "TFColor = _TFColor()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "e9u6sQuaLCuh"
      },
      "source": [
        "### Compute Probabilities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PzMNEA2LLEiY"
      },
      "source": [
        "Compute the probabilities of possible credit card interest rates in 12 months."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "1tLQMGHeLFtT"
      },
      "outputs": [],
      "source": [
        "# First we encode our assumptions.\n",
        "num_times_fed_meets_per_year = 8.\n",
        "possible_fed_increases = tf.range(\n",
        "    start=0.,\n",
        "    limit=num_times_fed_meets_per_year + 1)\n",
        "possible_cc_interest_rates = 2. + 10. + 0.25 * possible_fed_increases \n",
        "prob_fed_raises_rates = tf.constant([0.6, 0.7, 0.8, 0.9])  # Wild guesses.\n",
        "\n",
        "# Now we use TFP to compute probabilities in a vectorized manner.\n",
        "# Pad a dim so we broadcast fed probs against CC interest rates.\n",
        "prob_fed_raises_rates = prob_fed_raises_rates[..., tf.newaxis]\n",
        "prob_cc_interest_rate = tfd.Binomial(\n",
        "    total_count=num_times_fed_meets_per_year,\n",
        "    probs=prob_fed_raises_rates).prob(possible_fed_increases)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "37ZjdqA8LISQ"
      },
      "source": [
        "### Execute TF Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "X6DVo4YzLIq4"
      },
      "outputs": [],
      "source": [
        "# Convert from TF to numpy.\n",
        "[\n",
        "    possible_cc_interest_rates_,\n",
        "    prob_cc_interest_rate_,\n",
        "    prob_fed_raises_rates_,\n",
        "] = evaluate([\n",
        "    possible_cc_interest_rates,\n",
        "    prob_cc_interest_rate,\n",
        "    prob_fed_raises_rates,\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WKj9nGWuLL2w"
      },
      "source": [
        "### Visualize Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "height": 678
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1132,
          "status": "ok",
          "timestamp": 1538773550021,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "kzXDZJhZLNMn",
        "outputId": "9973a8e9-90fb-4f4d-ff93-05ec7280a86a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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2GEpu3bqF7t27o3HjxujRo4faP8qioqLQuXNnuLi4oH379jrb/xHpCvN44ZjH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\nlt/rD9t4s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XrzzTfrff9g+2f27NkqLy9vUC2vv/66+vbtq9jYWOXk5GjHjh1auXKlunbt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\nAwAAAABgIxpxAAAAAABs9P8BjrtAtyyhn78AAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f7c736ae090\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(14, 9))\n",
        "for i, pf in enumerate(prob_fed_raises_rates_):\n",
        "  plt.subplot(2, 2, i+1)\n",
        "  plt.bar(possible_cc_interest_rates_,\n",
        "          prob_cc_interest_rate_[i],\n",
        "          color=TFColor[i],\n",
        "          width=0.23,\n",
        "          label=\"$p = {:.1f}$\".format(pf[0]),\n",
        "          alpha=0.6,\n",
        "          edgecolor=TFColor[i],\n",
        "          lw=\"3\")\n",
        "  plt.xticks(possible_cc_interest_rates_ + 0.125, possible_cc_interest_rates_)\n",
        "  plt.xlim(12, 14.25)\n",
        "  plt.ylim(0, 0.5)\n",
        "  plt.ylabel(\"Probability of cc interest rate\")\n",
        "  plt.xlabel(\"Credit card interest rate (%)\")\n",
        "  plt.title(\"Credit card interest rates: \"\n",
        "            \"prob_fed_raises_rates = {:.1f}\".format(pf[0]));\n",
        "  plt.suptitle(\"Estimates of credit card interest rates in 12 months.\",\n",
        "               fontsize=\"x-large\",\n",
        "               y=1.02)\n",
        "  plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "tfpp-y5wW7Pd"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Credit_Card_Interest.ipynb",
      "provenance": [
        {
          "file_id": "1w4FZslgiCCMWUT7NbGi6fnSPY6YhTSDJ",
          "timestamp": 1537383195517
        }
      ],
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
